Remove 2016 Remove Experimentation Remove Machine Learning
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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

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Autodesk enlists Einstein AI to enhance customer service

CIO Business Intelligence

Salesforce first launched Einstein in 2016 , but the AI platform has evolved and expanded to address many common business tasks for specific audiences in the years since, including sales and marketing, e-commerce, and other routine but vital corporate functions. “We But at this point, we have not launched any of these capabilities.”

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Einstein Studio 1: What it is and what to expect

CIO Business Intelligence

The company has been bundling various forms of automation into its Einstein brand since 2016. This year, however, Salesforce has accelerated its agenda, integrating much of its recent work with large language models (LLMs) and machine learning into a low-code tool called Einstein 1 Studio. This isn’t a new push for Salesforce.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline?

Sanjeev Mohan

My journey in helping our customers with their technical queries started when I joined Gartner in late 2016. I saw the winds change and the inquiry requests shifted towards advanced analytics involving machine learning (ML) questions. This is the focus of my latest research which published in Jan 2019. I am glad you asked.

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Join DataRobot at Big Data & AI Paris 2022

DataRobot Blog

Since 2016, DataRobot has aligned with customers in finance, retail, healthcare, insurance and more industries in France with great success, with the first customers being leaders in the insurance space. . Organizations are no longer satisfied with “experimental” AI, they want AI implemented in business processes that drive results at scale.

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AWS: Moving Beyond Infrastructure to Monetize its Ecosystem

Hurwitz & Associates

I would divide the announcements (too many to list) into four buckets: Alexa for Business; enterprise expansion; support for Kubernetes, and AI/machine learning Tools. Machine Learning and AI take center stage. In 2016 Amazon announced that the Amazon AI platform as a way to bring AI tools to its developer community.

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